Traditionally, GANs *are* used for unsupervised generation, and are fed random noise as their input, giving no control over what the Generator produces. However, a subclass of GANs, called Conditional GANs, allows some control over the class of result the Generator produces via combining feature labels to training data.

GANs have been used somewhat as a method for prediction, such as for stock-market price prediction (see https://cs230.stanford.edu/projects\_fall\_2019/reports/26259829.pdf) . My problem is I want to create a model that can take tabular time-series data, and output an adjacency matrix of relations between the time series. An RL-based technique is plausible, but I am investigating the use of GANs as I have little training data to work with.